Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to conclude that ML and its close cousin deep learning (DL) have ushered unprecedented developments in all areas of physical sciences especially chemistry. Not only the classical variants of ML , even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionzed material design and performance of photo-voltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is to not only to foster exposition to the aforesaid techniques but also to empower and promote cross-pollination among future-research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.
Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to conclude that ML and its close cousin deep learning (DL) have ushered unprecedented developments in all areas of physical sciences especially chemistry. Not only the classical variants of ML , even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionzed material design and performance of photo-voltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is to not only to foster exposition to the aforesaid techniques but also to empower and promote cross-pollination among future-research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.
Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.
Quantum computing plays a significant role in simulating molecules and atoms and offers advantages in chemistry over classical computing. The potential of Quantum Machine Learning (QML) can be used in drug discovery, chemical reaction simulations, and Material design for pharmaceuticals. QML leverages quantum computing and advanced machine learning to accelerate the identification of drug candidates, predict molecular interactions, and optimize compounds. In this paper, we present a systematic review of the methods used for molecular property prediction and molecular generation using quantum machine learning. We have included the recent research, perspective, advantages, and challenges that must be addressed to achieve this task. The objective of this research is not only to discuss current strategies and methods used for drug discovery but also to promote interdisciplinary research in the field of quantum computing and chemistry for wellness.
Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect on computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain. We also elaborate upon the recent progress of different quantum machine learning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.
Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials design. At the same time, quantum computing is promoted as chemistry’s “killer application,” with the promise of solving many body problems beyond classical reach. Between these poles lies quantum machine learning (QML), a rapidly developing field that integrates quantum algorithms with ML paradigms. But is QML today a revolution, or merely hype? This article surveys the current state of classical ML in computational chemistry, highlighting its extraordinary successes and real limitations of transferability, interpretability, and generalization. We then examine the current landscape of quantum computing hardware and algorithms, clarifying what has actually been achieved with NISQ devices and where fundamental roadblocks remain. Building from this foundation, we analyze the foundations of QML algorithms—quantum kernels, variational quantum circuits, and hybrid workflows—and their putative advantages in polynomial scaling and Hilbert space expressivity. To ground discussion in practice, we present a case study of H₂ molecular energies, contrasting classical ML’s near instantaneous accuracy with quantum algorithms like the Variational Quantum Eigensolver. The outcome is telling: performance parity, not advantage, achieved only for toy systems under heavy simplification. This provides proof of principle for QML’s viability while simultaneously underscoring the severe constraints of current hardware. From this synthesis, we conclude that QML is presently characterized more by hype than utility, given the industrial dominance of classical ML. Yet in the long term—likely the mid 2030s and beyond—fault tolerant quantum hardware could transform computational chemistry, especially for strongly correlated systems where classical surrogates fail. The future lies not in premature claims, but in hybrid strategies, honest benchmarks, interpretable models, and sustainable integration across classical ML and quantum approaches. QML is not today’s solution. It is tomorrow’s horizon. And whether it becomes a true game changer will depend not on marketing cycles, but on the community’s ability to bridge hype with the patient, long term engineering of methods and machines.
Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information, quantum gravity, and large-scale numerical simulations. Recently, researchers interested in quantum matter and strongly correlated quantum systems have turned their attention to the algorithms underlying modern machine learning with an eye on making progress in their fields. Here we provide a short review on the recent development and adaptation of machine learning ideas for the purpose advancing research in quantum matter, including ideas ranging from algorithms that recognize conventional and topological states of matter in synthetic experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems. We discuss the outlook for future developments in areas at the intersection between machine learning and quantum many-body physics.
Abstract The rapid advancement of artificial intelligence has made machine learning (ML) methods central to modern science, technology, and industry. At the same time, the emergence of programmable quantum computers, along with the anticipation of large-scale fault-tolerant machines in the future, has generated interest in the potential of quantum machine learning (QML)—a field that explores quantum properties to enhance ML tasks. While QML is often considered a promising application for quantum computing, its practical advantages over classical methods remain an area of active research and exploration. In recent years, QML has garnered significant global attention, with researchers investigating its potential and limitations. In this chapter, we introduce the fundamentals of QML and provide an overview of recent developments and future trends in the field. We outline potential opportunities where quantum properties might offer advantages in ML tasks while also addressing the open challenges that QML faces in terms of scalability, implementation, and practical utility. We also explore the potential applications of QML in defense and security-sensitive domains. Here, researchers speculate that integrating quantum computing into ML could lead to the development of systems capable of resisting sophisticated threats, such as data manipulation and poisoning. However, realizing these possibilities requires further advancements in both quantum hardware and algorithmic design, making QML a promising yet still evolving field.